The Best Way to Do Code Reviews in GitHub (with Real Examples and Tools)

Introduction

Code reviews are one of the most valuable habits a development team can build. They help catch bugs before they reach production, improve code quality, and encourage knowledge sharing across the team. For newer developers, it's a chance to learn best practices. For senior engineers, it's a way to maintain architectural consistency and prevent technical debt.

In most modern workflows, code reviews happen through pull requests (PRs), and GitHub has become the go-to platform for this. Whether you're working in a fast-moving startup or a large enterprise, chances are you're using GitHub to collaborate, track changes, and merge code into shared branches.

But here’s the catch: not all code reviews are equal. Some are rushed. Some miss critical issues. Others get bogged down in nitpicks while overlooking bigger architectural concerns. And sometimes, reviews are delayed for days simply because everyone is too busy.

That's where a thoughtful approach to code reviews and the right tools can make all the difference. Manual reviews are important, but combining them with automation can help teams move faster without sacrificing quality. Tools like CodeAnt.ai can automate the tedious parts of a review like catching styling issues, detecting security flaws, or identifying repetitive patterns so humans can focus on what they do best: reviewing logic, intent, and maintainability.

In this guide, we'll walk through the best way to do code reviews in GitHub, explain different types of code reviews, share best practices, and show how automation tools like CodeAnt.ai can make the whole process faster and more effective. Whether you're new to code reviews or want to level up your team's workflow, this guide is for you.

What is Code Review?

Code review is when teammates take time to look at someone’s code changes to make sure everything works well, makes sense, and fits the way the team writes software.

At its core, a good code review:

• Validates correctness and functionality

• Encourages simplicity and readability

• Ensures alignment with architectural guidelines

• Acts as a feedback and learning opportunity for everyone involved

What is Code Review in GitHub?

In GitHub, code reviews happen through pull requests. When a developer finishes a feature or bug fix, they push their changes to a separate branch and open a pull request (PR) to merge it into the main branch. Other team members are then invited to review the changes, leave comments, request improvements, or approve the code.

This process helps teams:

• Catch bugs early

• Maintain consistent code style and structure

• Share knowledge about parts of the codebase

• Encourage collaboration and discussion

Here’s how it usually looks in practice:

1. A developer pushes code to a feature branch.
   
   
   

2. They open a PR with a description of what was changed and why.
   
   
   

3. Teammates review the code in the “Files Changed” tab.
   
   
   

4. Reviewers can leave inline comments, ask questions, or suggest changes.
   
   
   

5. Once the code is approved (and passes any checks), it gets merged into the main branch.
   
   
   

Code review on GitHub is built into the development flow, but just using the tool doesn’t guarantee effective reviews. How the review is done, the quality of the comments, the process, and the consistency makes all the difference.

Key GitHub features for code review include:

• Inline comments: Discuss specific lines of code
  
  
  

• Review summaries: Approve, comment, or request changes
  
  
  

• Draft PRs: Share work-in-progress for early feedback
  
  
  

• Code owners: Auto-assign reviewers based on file paths
  
  
  

• Suggested changes: Easily commit small edits from comments
  
  
  

• Checks and CI/CD: Ensure tests, linters, and builds pass before merge

GitHub reviews are central to modern development workflows and with intelligent tools like Codeant AI, they can become faster, deeper, and more educational for everyone involved.

In the next section, we’ll look at the types of code reviews, including how modern teams blend manual and automated review approaches for better results.

Types of Code Reviews

Code reviews come in various formats, each suited to different team dynamics and development workflows. Choosing the right type or a mix can help your team balance speed, quality, and collaboration. Here are the most common types of code reviews used by teams today.

1. Asynchronous Reviews (Pull Request-Based)

This is the most widely used code review format, especially in teams using Git-based workflows. Once a developer pushes changes to a feature branch, they open a pull request (PR) on GitHub. Reviewers can then go through the code on their own schedule, leave comments, ask questions, or suggest improvements.

Common Tools:

• GitHub (built-in code review, comments, suggestions)

• GitLab, Bitbucket, Azure DevOps

• Code review bots (e.g., Reviewpad, Mergify)

Advantages:

• Flexible timing for distributed teams

• Clear feedback history and traceability

• Can integrate automated CI checks, linters, and tests

Typical Use Case:

Most production teams, especially those working remotely or across time zones.

2. Synchronous Reviews (Live or Pair Programming)

Synchronous reviews happen in real time, often during pair programming sessions or live walkthroughs. Two or more developers go through code together, discussing logic and implementation as they go.

Common Tools:

• Visual Studio Code Live Share, JetBrains Code With Me

• Tuple, Zoom or Google Meet

Advantages:

• Immediate feedback and clarification

• Collaborative problem-solving

• Great for mentoring

Typical Use Case:

Complex or high-impact features, design reviews, mentoring sessions, or small, fast-moving teams.

3. Over-the-Shoulder Reviews

This is a quick and informal type of review, where one developer simply asks another to take a look at their code either in person or via screen share. It's useful for fast iteration but lacks the documentation and traceability of formal reviews.

Common Tools:

• In-person at a teammate’s desk

• Slack screen share, Zoom, Google Meet
  
  
  

Advantages:

• Low overhead, fast and personal feedback

• Encourages team communication
  
  
  

Limitations:

• No audit trail or formal record of decisions

• Easy to skip critical checks if rushed
  
  
  

Typical Use Case: Early-stage startups, co-located teams, quick fixes, or prototyping.

4. Tool-Assisted or AI-Augmented Reviews

Common Tools:

• CodeAnt.ai: Context-aware code review assistant that flags architectural issues, missed test coverage, and offers insightful, educational feedback
  
  

• GitHub Copilot: AI pair programmer that helps during coding
  
  

• SonarQube, Codacy, DeepSource: Static analysis platforms
  
  

• Reviewpad, Danger, lint-staged: Automated workflow tools for pull requests

Advantages:

• Reduces reviewer fatigue and repetitive feedback
  
  

• Promotes consistency and coding standards
  
  

• Supports learning and team growth

Typical Use Case:

Fast-growing teams, large codebases, organizations looking to scale reviews with automation and AI support.

With these types of reviews in mind, the key is to choose what works best for your team’s size, style, and structure. Many high-performing teams combine asynchronous reviews with AI-assisted tools like CodeAnt.ai for a more scalable, reliable process.

Next, we’ll explore what actually makes a “good” code review, and how to ensure your team isn’t just checking boxes, but really improving code quality.

What Makes a Good Code Review?

Not all code reviews are created equal. A good code review doesn’t just check for syntax errors it ensures the code is understandable, maintainable, and aligned with the project’s goals. It’s a balance of technical correctness, thoughtful collaboration, and shared ownership of quality.

Here’s what separates a good review from a rushed or unhelpful one:

1. Clarity Over Cleverness

Good reviewers focus on whether the code is easy to understand, not just whether it works. Clever one-liners or overly abstract solutions might save a few lines but can cost the team in maintainability. A helpful review might include questions like:

• "Can this be made more readable?"
  
  

• "Could we simplify this logic without changing behavior?"

2. Consistent Standards

Sticking to consistent naming, formatting, and patterns helps the whole team stay aligned. Rather than pointing out every missing space or inconsistent bracket manually, it's better to use automation for style enforcement this is where tools like CodeAnt.ai can automatically flag stylistic inconsistencies.

3. Constructive Feedback

A good review is kind and collaborative. Instead of saying, "This is wrong," a reviewer might say:

• “Could we use a more descriptive name here?”
  
  

• “This looks great one small thing we might improve...”

Tone matters. Code reviews should feel like teamwork, not gatekeeping.

4. Context Awareness

Before commenting, great reviewers ask themselves:

• Does this code belong in this part of the application?
  
  
  

• Does it follow the project’s architectural patterns?
  
  
  

• Is this solving the right problem, or just working around it?
  
  
  

If reviewers lack context, it’s okay to ask questions instead of assuming.

5. Focus on the Big Stuff First

A common mistake in reviews is jumping straight into minor nits (like spacing or naming) before addressing larger design or logic issues. It's more effective to start with:

• Functionality

• Test coverage

• Scalability

• Security concerns

Then, once the core is solid, polish the smaller details.

6. Right-Sized Reviews

The best reviews happen in small, manageable pull requests. Reviewing hundreds of lines at once increases the chance of missed issues. Encourage contributors to break large features into smaller PRs when possible.

7. Pairing Automation with Human Insight

Automation tools can handle the repetitive and surface-level checks such as formatting, security linting, and unused code. This allows reviewers to spend their time where it matters most: business logic, usability, and architecture.

Example:

A reviewer might miss a repeated code pattern, but CodeAnt.ai can catch it instantly and suggest a reusable helper function. This kind of support turns every review into a learning opportunity, especially for junior developers.

Real-World Impacts of Effective Code Reviews

With Practical Examples from Teams of All Sizes

Well-structured code reviews aren’t just a “nice to have.” When done right and especially when paired with automation or AI they directly improve team speed, code quality, onboarding, and developer satisfaction.

Here are a few real-world examples from large enterprises and high-growth startups that show what’s possible.

1. Accenture + GitHub Copilot

Scenario:
Accenture ran a large-scale experiment with ~1,000 developers using GitHub Copilot to evaluate how AI-assisted development impacts performance.

Results:

• Developers completed coding tasks up to 55% faster
  
  

• 85% reported more confidence in code quality
  
  

• 90% said they felt more satisfied with their work
  
  
  

Source: GitHub Research Blog

2. ANZ Bank + GitHub Copilot

Scenario:
In a six-week internal trial, over 1,000 engineers at ANZ Bank were divided into AI-assisted vs. manual groups to measure productivity in a regulated, high-risk environment.

Results:

• Teams using Copilot saw measurable gains in productivity and code quality
  
  
  

• Feedback was overwhelmingly positive on workflow support and team velocity
  
  
  

Source:arXiv Research Paper

3. Meridian Solutions + RepoVox

Scenario:
This FinTech startup scaled rapidly from 4 to 24 engineers in under 18 months. With growing PR queues and knowledge silos, they adopted RepoVox, an AI-powered GitHub assistant for summarization and review support.

Results after 3 months:

• 42% faster PR turnaround (27h → 15.7h)

• 204% more bugs caught before merging

• 67% less time spent in recurring review meetings

• 42% faster onboarding for junior devs

• 68% increase in meaningful review comments per PR
  
  
  

Source: RepoVox Case Study

Summary of Real‑World Outcomes

Why This Matters for Your Team

• Scalable Results: Whether you're a large enterprise or a startup, structured reviews and AI tools scale well with team growth.

• Cross-Team Benefits: These tools support faster development, better onboarding, and fewer back-and-forths in review.
  
  
  

• Proven ROI: These aren’t just best practices they’re cost-effective, measurable improvements.
  
  
  

If your team is still relying on ad hoc or inconsistent reviews, these examples are a reminder: reviewing code effectively can create a multiplier effect across your entire engineering organization.

In the next section, we’ll walk through how to put these principles into action, starting with the step-by-step GitHub review process.

Step-by-Step: Reviewing Code on GitHub

GitHub’s pull request (PR) system is the standard way teams review code. It gives reviewers everything they need to inspect, comment, and approve code changes in one place. Whether you're working solo or in a large team, mastering the PR workflow ensures better quality, fewer bugs, and smoother merges.

Here’s a step-by-step breakdown of how to review code effectively in GitHub with notes on where tools like CodeAnt.ai can enhance the process.

1. Receive or Assign the Pull Request

Once a PR is opened, the author should assign reviewers or request them via GitHub’s UI. Reviewers are typically teammates familiar with the relevant part of the codebase.

Best Practices:

• Rotate reviewers so knowledge spreads across the team

• Avoid assigning too many reviewers, 1–2 focused reviewers are usually enough

• Add context in the PR description to help reviewers understand “why” the changes exist

2. Understand the Scope

Before diving into the code, review:

• The PR title and description

• Related issue links, Jira tickets, or design docs

• Test plan or screenshots, if UI-related

This sets context and prevents misunderstandings.

3. Review the Code in "Files Changed" Tab

Now move to the code itself. Look at:

• Logic and correctness

• Edge cases or unhandled scenarios

• Code readability and naming

• Test coverage (are new tests included?)

• Security, performance, and scalability considerations

Pro Tip: Start with the big picture before going into line-by-line feedback.

4. Leave Constructive Comments

Use GitHub’s inline comment feature to call out:

• Bugs or logic issues

• Suggestions for naming, structure, or performance

• Questions about intent (“Could you clarify why we need this change?”)

Good Example: "Would it make sense to extract this into a utility function? I think we use a similar block in UserManager.js."

Poor Example: "This is wrong." (Too vague and unhelpful)

5. Approve, Comment, or Request Changes

Once you’re done reviewing:

• Click “Approve” if the code is good to go

• Choose “Request Changes” if major fixes are needed

• Leave “Comment” if you have feedback but don’t block the merge

Make sure the tone of your summary is respectful and clear. For example: "Looks great overall! Just a couple of small changes before we merge."

6. Where CodeAnt.ai Adds Value

During the review, CodeAnt.ai can:

• Automatically flag risky architectural changes or duplicated logic

• Suggest missing unit tests for critical paths

• Catch inconsistent patterns across large codebases

• Offer real-time feedback in the PR with context-aware explanations

This allows reviewers to focus on intent and design while CodeAnt.ai handles repetitive and easily missed checks.

Example Scenario: A PR includes a silent fail case (try...except with no logging). CodeAnt.ai flags it with: "Potential issue: exception is swallowed without logging. Consider raising or logging to aid debugging."

This saves human reviewers from combing through dozens of lines to catch such subtle but critical issues.

7. Merge When Ready

Once all reviews are complete and checks pass (like CI builds or automated tools), the PR can be merged.

Best Practices:

• Use “Squash and merge” for a cleaner commit history

• Avoid merging if any review is unresolved or red flags remain

8. Follow Up After Merge

If the change introduces new logic or affects others, notify your team. Update documentation, changelogs, or onboarding docs if needed.

This workflow keeps your GitHub code reviews productive, respectful, and efficient. And by combining human insight with AI-assisted tools like CodeAnt.ai, your team can catch more issues, move faster, and grow more consistently.

Next, we’ll cover some best practices and anti-patterns to keep your team aligned.

Best Practices for Code Reviews

Code reviews aren’t just about catching bugs, they’re about improving code quality, team collaboration, and shared understanding. Here are key best practices that help teams get the most from every pull request.

1. Keep Pull Requests Small and Focused

Smaller pull requests are easier to review, less error-prone, and quicker to merge. Large PRs often overwhelm reviewers and delay feedback.

Tip: If a feature requires many changes, break it into smaller, logical PRs (e.g., “Setup,” “UI,” “API wiring”).

2. Review Regularly, Not Just When Asked

Don’t wait for requests—check the open PR queue daily. Teams that review code frequently move faster and build higher trust.

Idea: Set a daily time slot for reviewing code, like right after standup or before lunch.

3. Use GitHub’s Features to Your Advantage

GitHub offers a lot more than just inline comments:

• Suggested changes: Quickly recommend edits with one click

• Review summaries: Summarize your thoughts and decision clearly

• Pull request templates: Help authors provide context every time

Make use of these features to streamline feedback and improve clarity.

4. Ask Questions, Don’t Just Make Demands

Instead of ordering changes, ask for clarification. This builds trust and makes it easier for the author to engage with feedback.

Instead of: “Use a different algorithm.”

Try: “Is there a reason you chose this approach? Wondering if X might be simpler or faster.”

5. Don’t Nitpick (Let Automation Handle That)

Focus on meaningful feedback—logic, architecture, test coverage—not trivial style issues. Tools like linters or CodeAnt.ai can handle spacing, naming, and formatting automatically.

Let automation reduce the mental load and keep reviews centered on what matters most.

6. Be Kind and Collaborative

Even if the code isn’t great, your feedback should be. Everyone is learning—including senior developers. Encourage better code, don’t shame people into it.

Constructive tone: “Thanks for tackling this—it’s a tricky area. One thought: would adding a check here avoid potential null issues?”

7. Balance Speed with Thoroughness

While quick reviews are ideal, never rush just to “unblock.” Take enough time to understand the change and ensure quality, especially for complex or high-risk areas.

8. Give Positive Feedback, Too

If a solution is clever or a refactor is helpful, say so. A well-placed “nice catch” or “great abstraction” boosts morale and reinforces good practices.

9. Pair AI with Human Judgment

AI-powered tools like CodeAnt.ai can catch issues before you even start reading. Use these tools to flag potential bugs, missed tests, or risky design patterns, then validate them with your own review. You’ll move faster without cutting corners.

10. Review the Right Things

Focus your review time on:

• Functionality

• Logic and architecture

• Security and edge

• Test completeness

• Maintainability and clarity

Don’t waste cycles arguing over where the brackets go. That’s what formatters are for.

Up next: we’ll walk through common mistakes to avoid (aka “code review anti-patterns”) so your team knows not just what to do, but what to avoid.

Common Code Review Anti-Patterns (What to Avoid)

Even with the right tools and intentions, it's easy to fall into bad code review habits that slow teams down or create unnecessary friction. These are some of the most common anti-patterns and what to do instead.

1. Drive-By Approvals

Approving a pull request without reading it, just to “unblock” someone can be risky, especially if the code affects shared logic or sensitive areas.

What it looks like:

“LGTM” (Looks Good To Me) with no comments or questions, even on a big PR.

What to do instead:

Take the time to understand the change. Even a quick summary of what you checked ("Reviewed logic, tests look good") shows intent.

2. PR Ping-Pong

This happens when a PR goes back and forth between reviewer and author for multiple small rounds of feedback, often due to unclear expectations.

What it looks like:

Fix this → change again → another nit → minor rename → still not right…

What to do instead:

If you’re on the third round of comments, it’s time to talk. Hop on a quick call or screen share to resolve things faster.

3. Over-Ownership

Reviewers pushing their personal coding style or preferences when there's no team convention in place.

What it looks like:

“I like this written as a one-liner.”
“You should use forEach instead of map just because.”

What to do instead:

Stick to the project’s conventions. If there aren’t any, suggest starting a team discussion not enforcing your own taste in someone else’s PR.

4. Merging Without Review

Sometimes developers merge without waiting for feedback either because they’re in a rush or the reviewer is unavailable.

What it looks like:

“Just merged it. It was a small change anyway.”

What to do instead:

Unless it’s an emergency fix or a solo project, don’t skip reviews. Even small changes can have unintended effects. If speed is a concern, use automation (like CodeAnt.ai) to provide fast, initial feedback.

5. Focusing Only on Style

Over-commenting on spacing, naming, or formatting while missing logic bugs or unclear architecture.

What it looks like:

10 comments on tabs vs spaces, 0 comments on broken pagination logic

What to do instead:

Let tools (linters, formatters, CodeAnt.ai) handle style. Focus your review on logic, structure, performance, and readability.

6. Unclear or Vague Feedback

Leaving comments that don’t help the author know what to do or why something needs to change.

What it looks like:

“This is bad.”
“Fix this.”

What to do instead:

Be specific and kind. For example:

“This can throw a null error if user is undefined. Can we add a check or default?”

7. Ignoring Automation Feedback

If your CI pipeline or AI review assistant flags an issue, don’t skip over it just because it wasn’t written by a human.

What it looks like:

CodeAnt.ai flags a missing null check → reviewer ignores it and merges anyway

What to do instead: 

Treat automated suggestions as part of the team. Review, verify, and respond to them like you would a teammate’s comment.

Avoiding these habits can drastically improve your team’s code review culture. It reduces frustration, speeds up turnaround time, and creates a more respectful, effective development environment.

Conclusion

Code reviews can either feel like a frustrating chore or become one of your team’s greatest strengths. The difference lies in the process, mindset, and tools you bring to the table.

With GitHub’s built-in pull request features, a thoughtful review culture, and the support of intelligent tools like CodeAnt.ai, you don’t have to choose between speed and quality. You can have both.

Here’s what we’ve covered:

• What code reviews are, why they matter, and how they work in GitHub

• Different types of reviews async, live, AI-assisted and when to use them

• What makes a review truly effective: clarity, consistency, kindness, and context

• Step-by-step instructions to run clean, helpful reviews in GitHub

• Real-world proof that AI-assisted reviews improve productivity and quality

• Common mistakes to avoid so your team doesn’t fall into bad habits

What to Do Next

If you’re ready to level up your code review process:

• Start using a review checklist to bring consistency across the team

• Break down large PRs into smaller, focused changes

• Automate repetitive checks using linters, test runners, and tools like CodeAnt.ai

• Encourage reviewers to ask questions, give thoughtful feedback, and recognize good work

Better code reviews don’t require a full rewrite of your workflow. Often, it’s about a few small changes done consistently and supported by the right tools.

Try applying these tips to your next GitHub pull request. Or better yet, invite CodeAnt.ai into the review process and see what kind of issues it catches before you even hit “request review.”